Insights into Chinese Canadian culture: enablers and barriers for fruit and vegetable intake
Bibliographic record
Abstract
Background: Fruits and vegetables (F&V) play a vital role in promoting health and preventing diseases. Numerous studies have demonstrated the association between F&V consumption and reduced risks of cardiovascular disease, cancer, and mortality. Despite the high priority of public health in promoting F&V intake, Chinese immigrants in Canada often fall below national guidelines in their consumption. Understanding the factors influencing F&V intake in this community is crucial for developing effective interventions. Methods: This study used an applied ethnographic research approach to gain insight into the enablers and barriers that influence F&V intake among Chinese-Canadian adults in Richmond, BC. Semi-structured interviews and 'photovoice' group sessions were conducted to gather qualitative data from community participants and health care providers (HCPs). Results: The research identified four key themes: (1) Cultural differences around how vegetables are perceived, consumed and prepared; (2) Motivators and strategies for increasing vegetable and fruit intake; (3) Lack of culturally relevant dietary education and resources; and (4) Importance of value in vegetable/fruit-related decisions. Participants showed a strong preference for the traditional Eastern diet, with cost of food and lack of knowledge about Western vegetables acting as barriers to dietary diversity. The study also highlighted the need for culturally tailored educational resources to effectively promote F&V consumption. Conclusion: By adopting a multi-modal approach, incorporating both interviews and 'photovoice' sessions, this research provided comprehensive insights into the participants' perspectives and experiences related to F&V intake. Understanding these factors can guide the development of culturally appropriate interventions to increase F&V consumption among Chinese-Canadian adults in Richmond, BC, and potentially improve their overall health and well-being. Future studies should consider the heterogeneity within the Chinese immigrant population and target a more balanced representation of age groups to further enhance our understanding of F&V intake patterns in this community.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.021 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".